Systems and methods to process electronic images to adjust attributes of the electronic images

A method for providing color constancy among whole slide images, the method comprising:

• receiving, as a source image input to a trained machine learning system, a whole slide image from a population of whole slide images;
• receiving, as a target image input to the trained machine learning system, a template having a set of color characteristics, the template comprising the population of whole slide images;
• executing, by the trained machine learning system, a color normalization process to map the set of color characteristics of the template to the whole slide image to generate a normalized image comprised of the whole slide image adjusted to correspond to the set of color characteristics of the template;
• and
• providing, as output of the trained machine learning system, the normalized image.

A non-transitory computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform operations for providing color constancy among whole slide images, the operations comprising:

• receiving, as a source image input to a trained machine learning system, a whole slide image from a population of whole slide images;
• receiving, as a target image input to the trained machine learning system, a template having a set of color characteristics, the template comprising the population of whole slide images;
• executing, by the trained machine learning system, a color normalization process to map the set of color characteristics of the template to the whole slide image to generate a normalized image comprised of the whole slide image adjusted to correspond to the set of color characteristics of the template;
• and
• providing, as output of the trained machine learning system, the normalized image.